Hypothesis testing and statistical significance: Practice Questions — Descriptive Analysis and Visualization (NVIDIA-Certified Associate: Accelerated Data Science)

Practice Questions on Hypothesis Testing and Statistical Significance

These multiple-choice questions are designed to help you prepare for the Hypothesis Testing and Statistical Significance portion of the NVIDIA-Certified Associate: Accelerated Data Science exam. Each question includes four options, the correct answer, and a brief explanation.

1. Question 1: What is the primary purpose of a null hypothesis in hypothesis testing?

   • A) To prove the alternative hypothesis is true
   • B) To provide a statement of no effect or no difference
   • C) To determine the sample size
   • D) To calculate the p-value

   Correct Answer: B

   Explanation: The null hypothesis (H0) represents a default position that there is no effect or no difference. Hypothesis testing evaluates whether there is enough evidence to reject H0 in favor of the alternative hypothesis.

2. Question 2: If a p-value is less than the significance level (α = 0.05), what conclusion should be drawn?

   • A) Fail to reject the null hypothesis
   • B) Accept the null hypothesis
   • C) Reject the null hypothesis
   • D) Increase the sample size

   Correct Answer: C

   Explanation: A p-value below the significance level indicates that the observed data is unlikely under the null hypothesis, so we reject H0, suggesting the results are statistically significant.

3. Question 3: Which of the following best describes a Type I error?

   • A) Rejecting a true null hypothesis
   • B) Failing to reject a false null hypothesis
   • C) Accepting the alternative hypothesis when it is false
   • D) Increasing the sample variance

   Correct Answer: A

   Explanation: A Type I error occurs when the null hypothesis is true but is incorrectly rejected, leading to a false positive conclusion.

4. Question 4: In a two-tailed test, which p-value range would lead to rejecting the null hypothesis at α = 0.01?

   • A) p = 0.02
   • B) p = 0.005
   • C) p = 0.015
   • D) p = 0.03

   Correct Answer: B

   Explanation: For α = 0.01, only p-values less than 0.01 lead to rejection of H0. Here, p = 0.005 is less than 0.01, so we reject the null hypothesis.

5. Question 5: Which statement about statistical significance is true?

   • A) Statistical significance implies practical importance
   • B) A large sample size can lead to statistically significant results even with small effects
   • C) Statistical significance guarantees the alternative hypothesis is true
   • D) Statistical significance means the null hypothesis is definitely false

   Correct Answer: B

   Explanation: Large samples can detect very small differences that are statistically significant but may not be practically meaningful. Statistical significance does not guarantee practical importance or absolute truth.

6. Question 6: What does a confidence interval that does not include zero imply in the context of hypothesis testing?

   • A) The null hypothesis is likely true
   • B) The parameter estimate is not statistically significant
   • C) The null hypothesis can be rejected at the chosen confidence level
   • D) The sample size is too small

   Correct Answer: C

   Explanation: If a confidence interval for a parameter (e.g., difference of means) does not include zero, it suggests the effect is statistically significant and the null hypothesis of no effect can be rejected.

7. Question 7: Which of the following is NOT a valid assumption for conducting a parametric hypothesis test?

   • A) Data are independent and identically distributed
   • B) The population distribution is approximately normal
   • C) The sample size is extremely small without normality
   • D) Variances of groups are equal (homoscedasticity)

   Correct Answer: C

   Explanation: Parametric tests generally require normality or large sample sizes to approximate normality. Very small samples without normality violate assumptions and may invalidate the test.

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